Plant Disease Classification Using Tensorflow/Keras

This project is a plant disease classification project using Convolutional Neural Networks (CNN). The dataset used in this project is the PlantVillage Dataset.

Installation

1. Clone the repository

git clone https://github.com/Vignesh142/Plant_Disease_Prediction

2. Install the required libraries

pip install -r requirements.txt

3. Run the Website

uvicorn main:app --reload --port 3000

4. Open the Website

http://localhost:3000

Usage

1. Open the Website
2. Upload an image of a plant leaf
3. The model will classify the plant disease and display the result

Directory Structure

E2E_Potato_Disease_Classification
│   src
│   │   components
│   │   │   data_ingestion.py
│   │   │   data_transformation.py
│   │   │   model_trainer.py
│   │   pipeline
│   │   │   data_pipeline.py
│   │   │   model_pipeline.py
|   api
|   |   main.py
|   |   static
|   |   |   home.html
|   |   |   images
|   |   |   |   image.jpg
|   models
│   │   version-1
│   │   │   model.pb
│   │   version-2
│   │   │   model.pb
│   utils
│   setup.py
│   requirements.txt

Dataset

The dataset used in this project is the PlantVillage Dataset. Only Potato Plant leafs are classfied. The dataset is divided into two parts: training and testing.

Model

The model used in this project is a Convolutional Neural Network (CNN). The model is trained on the training dataset and tested on the testing dataset. The model is evaluated on the testing dataset using accuracy and loss metrics.

Results

The model achieved an accuracy of 98.5% on the testing dataset. The model is able to classify plant diseases and healthy plants with high accuracy.

Conclusion

The model is able to classify plant diseases and healthy plants with high accuracy. The model can be used to detect plant diseases early and prevent crop loss.

References

1. PlantVillage Dataset